CN105159094B - The design method of vehicle active suspension LQG controller Optimal Control Forces - Google Patents
The design method of vehicle active suspension LQG controller Optimal Control Forces Download PDFInfo
- Publication number
- CN105159094B CN105159094B CN201510645787.1A CN201510645787A CN105159094B CN 105159094 B CN105159094 B CN 105159094B CN 201510645787 A CN201510645787 A CN 201510645787A CN 105159094 B CN105159094 B CN 105159094B
- Authority
- CN
- China
- Prior art keywords
- weight coefficient
- design
- ride comfort
- vehicle
- matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Vehicle Body Suspensions (AREA)
- Tires In General (AREA)
Abstract
The present invention relates to the design method of vehicle active suspension LQG controller Optimal Control Forces, belong to Active suspension technical field.The present invention is according to 1/4 vehicle ride model, utilize MATLAB/Simulink, construct ride comfort weight coefficient optimization design Simulink simulation models, and using road roughness displacement as input stimulus, to take turns movement of the foetus displacement and suspension dynamic deflection as constraints, with the minimum design object of vehicle body Vertical Acceleration root-mean-square value, optimization design obtains ride comfort weight coefficient and LQG Optimal Control Forces.By example and simulating, verifying, the available accurately and reliably Active suspension LQG Optimal Control Forces of this method are that active suspension system designs and control provide reliable optimum control hydraulic design method.This method can not only improve the design level and product quality of active suspension system, improve the riding comfort and driving safety of vehicle, can also reduce product design and testing expenses.
Description
Technical field
The present invention relates to the design side of vehicle active suspension, particularly vehicle active suspension LQG controllers Optimal Control Force
Method.
Background technology
LQG controls are widely applied because having very strong applicability in active suspension system, wherein, optimal control
The determination of power processed is the key of LQG Controller of Active Suspension design.However, being understood according to institute's inspection information, at present both at home and abroad
For the design of vehicle active suspension LQG controller Optimal Control Forces, mostly it is the tendency according to designer to suspension property, presses
Primarily determine that LQG controls weight coefficient according to experience, then by multiple analog simulation, weighting system is progressively adjusted according to response quautity
Number, until obtaining satisfied output response quautity, and then designs the Optimal Control Force of LQG Controller of Active Suspension.Although utilizing
LQG controling powers obtained by this method, can make vehicle meet the requirement of current driving operating mode, however, designed controling power is simultaneously
Non-optimal.With the fast development and the continuous improvement of Vehicle Speed of Vehicle Industry, people to vehicle safety and
Riding comfort proposes higher requirement, the method for current LQG Controller of Active Suspension Optimal Control Force design, it is impossible to meet
The requirement that vehicle develops and Active suspension control device is designed.Therefore, it is necessary to set up a kind of accurate, reliable vehicle active suspension
The design method of LQG controller Optimal Control Forces, meets the requirement of vehicle development and the design of Active suspension control device, improves automobile
The design level and product quality of active suspension system, improve vehicle riding comfort and security；Meanwhile, reduce product design
And testing expenses, shorten the product design cycle.
The content of the invention
For defect present in above-mentioned prior art, the technical problems to be solved by the invention be to provide it is a kind of accurate,
The design method of reliable vehicle active suspension LQG controller Optimal Control Forces, its design flow diagram is as shown in Figure 1；1/4 vehicle
Ride illustraton of model is as shown in Figure 2.
In order to solve the above technical problems, vehicle active suspension LQG controller Optimal Control Forces provided by the present invention are set
Meter method, it is characterised in that use following design procedure：
(1) the 1/4 vehicle ride differential equation is set up：
According to vehicle single-wheel unsprung mass m1, sprung mass m2, suspension stiffness K2, tire stiffness Kt, active to be designed
Suspension manipulating forces Ua；With tire vertical deviation z1, vehicle body vertical deviation z2For coordinate；Swash by input of road roughness displacement q
Encourage；The 1/4 vehicle ride differential equation is set up, i.e.,：
(2) the state matrix A and control matrix B of LQG controls are determined：
According to vehicle single-wheel unsprung mass m1, sprung mass m2, suspension stiffness K2, tire stiffness Kt, vehicle traveling speed
Spend v, and filtering white noise road surface spatial-cut-off frequency n0c, the state matrix A and control matrix B of LQG controls are determined, is respectively：
(3) the weighting matrix expression formula of LQG controls is determined：
According to vehicle single-wheel unsprung mass m1, sprung mass m2, suspension stiffness K2, tire stiffness Kt, the spacing row of suspension
Journey [fd], and gravity acceleration g, it is determined that on ride comfort weight coefficient α1、α2、α3State variable, control variable and state
Variable and control variable cross-product term weighting matrix expression formula Q (α1,α2,α3)、R(α1,α2,α3)、N(α1,α2,α3), respectively
For：
Wherein,q3=1；α1It is the relative dynamic load weight coefficient of wheel, α2
It is the relative dynamic deflection weight coefficient of suspension, α3For vehicle body vertical vibration relative acceleration weight coefficient；
(4) Active suspension LQG controling powers U is determinedaExpression formula：
I steps：Choose the initial value of ride comfort weight coefficient, i.e. α1=k1、α2=k2、α3=k3, wherein, k1, k2, k3's
Value be less than more than zero 1 numerical value, and k1+k2+k3=1.0；
II steps：According to the ride comfort weighting coefficient initial values α chosen in I steps1=k1、α2=k2、α3=k3, and step
(3) the weighting matrix expression formula Q (α determined in1,α2,α3)、R(α1,α2,α3)、N(α1,α2,α3), calculating obtains weighting matrices Q
(k1,k2,k3)、R(k1,k2,k3)、N(k1,k2,k3)；
III steps：According to the weighting determined in the state matrix A and control matrix B, and II steps determined in step (2)
Matrix Q (k1,k2,k3)、R(k1,k2,k3)、N(k1,k2,k3), calculated using the LQR functions in Matlab and try to achieve Active suspension LQG
Control feedback gain matrix K；
IV steps：According to the feedback gain matrix K determined in III steps, with unsteadiness of wheels speedAnd tire vertical deviation
z1, body vibrations speedAnd vehicle body vertical deviation z2With road roughness displacement q as state variable, Active suspension LQG is determined
Controling power UaExpression formula, i.e.,：
Wherein,For matrixTransposed matrix；
(5) optimization design of ride comfort weight coefficient：
1. ride comfort weight coefficient optimization design simulation model is built
According to the 1/4 vehicle ride differential equation set up in step (1), and IV steps are tried to achieve in step (4)
Controling power Ua, using Matlab/Simulink simulation softwares, build ride comfort weight coefficient optimization design Simulink emulation
Model；
2. ride comfort weight coefficient optimization design object function is set up
According to the ride comfort weight coefficient optimization design Simulink simulation models set up in 1. step, with ride comfort plus
Weight coefficient α1、α2、α3For design variable, using road roughness displacement as input stimulus, vehicle ride situation is imitated
Very, the vehicle body Vertical Acceleration root-mean-square value obtained by emulation is utilizedSet up ride comfort weight coefficient optimization design mesh
Scalar functions Jo(α1,α2,α3), i.e.,：
3. ride comfort weight coefficient Constrained Conditions in Optimal Design is set up
According to vehicle single-wheel unsprung mass m1, sprung mass m2, tire stiffness Kt, gravity acceleration g, and the spacing row of suspension
Journey [fd], utilize tire vertical deviation z1, vehicle body vertical deviation z2, road roughness displacement q, and ride comfort weight coefficient α1、α2、
α3, ride comfort weight coefficient Constrained Conditions in Optimal Design is set up, i.e.,
4. the optimization design of ride comfort weight coefficient
According to the ride comfort weight coefficient optimization design Simulink simulation models set up in 1. step, and 3. in step
The ride comfort weight coefficient Constrained Conditions in Optimal Design set up, with ride comfort weight coefficient α1、α2、α3For design variable, with road
Face unevenness displacement is asked using optimized algorithm and ride comfort weight coefficient optimization design mesh is set up in 2. step as input stimulus
Scalar functions Jo(α1,α2,α3) minimum value, corresponding design variable is the optimum optimization design load of ride comfort weight coefficient,
That is α1o、α2o、α3o；
(6) LQG Controller of Active Suspension Optimal Control Force UaoDesign：
I steps：According to the ride comfort weight coefficient α that 4. optimization order design is obtained in step (5)1o、α2o、α3o, and step
(3) the weighting matrix expression formula Q (α determined in1,α2,α3)、R(α1,α2,α3)、N(α1,α2,α3), calculating obtains weighting matrices Q
(α1o,α2o,α3o)、R(α1o,α2o,α3o)、N(α1o,α2o,α3o)；
Ii steps：According to the weighting square determined in the state matrix A and control matrix B, and i steps determined in step (2)
Battle array Q (α1o,α2o,α3o)、R(α1o,α2o,α3o)、N(α1o,α2o,α3o), calculate to try to achieve using the LQR functions in Matlab and actively hang
Frame LQG optimum control feedback gain matrix Ko；
Iii steps：According to the Optimal Feedback gain matrix K determined in ii stepso, with unsteadiness of wheels speedAnd tire hangs down
To displacement z1, body vibrations speedAnd vehicle body vertical deviation z2With road roughness displacement q as state variable, it is determined that actively
The Optimal Control Force U of suspension LQG controllersao, i.e.,：
The present invention has the advantage that than prior art：
LQG controls are widely applied because having very strong applicability in active suspension system, wherein, optimal control
The determination of power processed is the key of LQG Controller of Active Suspension design.However, being understood according to institute's inspection information, at present both at home and abroad
For the design of vehicle active suspension LQG controller Optimal Control Forces, mostly it is the tendency according to designer to suspension property, presses
Primarily determine that LQG controls weight coefficient according to experience, then by multiple analog simulation, weighting system is progressively adjusted according to response quautity
Number, until obtaining satisfied output response quautity, and then designs the Optimal Control Force of LQG Controller of Active Suspension.Although utilizing
LQG controling powers obtained by this method, can make vehicle meet the requirement of current driving operating mode, however, designed controling power is simultaneously
Non-optimal.With the fast development and the continuous improvement of Vehicle Speed of Vehicle Industry, people to vehicle safety and
Riding comfort proposes higher requirement, the method for current LQG Controller of Active Suspension Optimal Control Force design, it is impossible to meet
The requirement that vehicle develops and Active suspension control device is designed.
The present invention utilizes MATLAB/Simulink, structure according to 1/4 vehicle ride model and Active suspension control power
Ride comfort weight coefficient optimization design Simulink simulation models have been built, and using road roughness displacement as input stimulus, to take turns
Movement of the foetus displacement and suspension dynamic deflection are constraints, excellent with the minimum design object of vehicle body Vertical Acceleration root-mean-square value
Change design and obtain ride comfort weight coefficient, and then design obtains Active suspension LQG Optimal Control Forces.By designing example and emulation
Contrast verification understands that the available accurately and reliably Active suspension LQG optimum controls force value of this method, is vehicle active suspension LQG
The design of Optimal Control Force provides reliable design method.Using this method, Vehicle Active Suspension System can be not only improved
Design level and product quality, improve vehicle riding comfort and security；Meanwhile, reduction product design and testing expenses, contracting
The short sawn timber design cycle.
Brief description of the drawings
It is described further below in conjunction with the accompanying drawings for a better understanding of the present invention.
Fig. 1 is the design flow diagram of vehicle active suspension LQG controller optimum control hydraulic design methods；
Fig. 2 is 1/4 vehicle ride illustraton of model；
Fig. 3 is the ride comfort weight coefficient optimization design Simulink simulation models of embodiment；
Fig. 4 is the simulation comparison curve of the vehicle body Vertical Acceleration time-domain signal of embodiment；
Fig. 5 is the simulation comparison curve of the vehicle body Vertical Acceleration power spectral density of embodiment.
Specific embodiment
The present invention is described in further detail below by an embodiment.
Certain vehicle single-wheel unsprung mass m1=40kg, sprung mass m2=320kg, suspension stiffness K2=20000N/m,
Tire stiffness Kt=200000N/m, bump clearance of suspension [fd]=100mm, gravity acceleration g=9.8m/s2, filter white noise
Road surface spatial-cut-off frequency n0c=0.011m-1, the LQG controling powers of Active suspension to be designed are Ua.The Active Suspension Design
Required Vehicle Speed v=72km/h, the controling power to the LQG Controller of Active Suspension is designed.
The design method for the vehicle active suspension LQG controller Optimal Control Forces that present example is provided, it designs stream
Journey figure is as shown in figure 1,1/4 vehicle ride illustraton of model is as shown in Fig. 2 comprise the following steps that：
(1) the 1/4 vehicle ride differential equation is set up：
According to vehicle single-wheel unsprung mass m1=40kg, sprung mass m2=320kg, suspension stiffness K2=20000N/
M, tire stiffness Kt=200000N/m, Active suspension control power U to be designeda；With tire vertical deviation z1, vehicle body vertical deviation z2
For coordinate；Using road roughness displacement q as input stimulus；The 1/4 vehicle ride differential equation is set up, i.e.,：
(2) the state matrix A and control matrix B of LQG controls are determined：
According to vehicle single-wheel unsprung mass m1=40kg, sprung mass m2=320kg, suspension stiffness K2=20000N/
M, tire stiffness Kt=200000N/m, Vehicle Speed v=72km/h, and filtering white noise road surface spatial-cut-off frequency n0c
=0.011m-1, the state matrix A and control matrix B of LQG controls are determined, is respectively：
(3) the weighting matrix expression formula of LQG controls is determined：
According to vehicle single-wheel unsprung mass m1=40kg, sprung mass m2=320kg, suspension stiffness K2=20000N/
M, tire stiffness Kt=200000N/m, bump clearance of suspension [fd]=100mm, and gravity acceleration g=9.8m/s2, it is determined that closing
In ride comfort weight coefficient α1、α2、α3State variable, control variable and state variable and control variable cross-product term weighting
Matrix expression Q (α1,α2,α3)、R(α1,α2,α3)、N(α1,α2,α3), it is respectively：
Wherein,q3=1；α1It is the relative dynamic load weight coefficient of wheel, α2For suspension
With respect to dynamic deflection weight coefficient, α3For vehicle body vertical vibration relative acceleration weight coefficient；
(4) Active suspension LQG controling powers U is determinedaExpression formula：
I steps：Choose the initial value of ride comfort weight coefficient；The embodiment chooses k1=0.1, k2=0.2, k3=0.7,
Wherein, k1+k2+k3=1.0, that is, choose the initial value α of ride comfort weight coefficient1=0.1, α2=0.2, α3=0.7；
II steps：According to the ride comfort weighting coefficient initial values α chosen in I steps1=0.1, α2=0.2, α3=0.7, and
The weighting matrix expression formula Q (α determined in step (3)1,α2,α3)、R(α1,α2,α3)、N(α1,α2,α3), calculating obtains weighting square
Battle array Q (0.1,0.2,0.7), R (0.1,0.2,0.7), N (0.1,0.2,0.7), i.e.,：
R (0.1,0.2,0.7)=9.766 × 10-6,
III steps：According to the weighting determined in the state matrix A and control matrix B, and II steps determined in step (2)
Matrix Q (0.1,0.2,0.7), R (0.1,0.2,0.7), N (0.1,0.2,0.7), are calculated using the LQR functions in Matlab and asked
Active suspension LQG control feedback gain matrix K is obtained, i.e.,：
K=[1941.2-925.1-14412.51 3653.0 11560.0]；
IV steps：According to the feedback gain matrix K determined in III steps, with unsteadiness of wheels speedAnd tire vertical deviation
z1, body vibrations speedAnd vehicle body vertical deviation z2With road roughness displacement q as state variable, Active suspension LQG is determined
Controling power UaExpression formula, i.e.,：
Wherein,For matrixTransposed matrix；
(5) optimization design of ride comfort weight coefficient：
1. ride comfort weight coefficient optimization design simulation model is built
According to the 1/4 vehicle ride differential equation set up in step (1), and IV steps are tried to achieve in step (4)
Controling power Ua, using Matlab/Simulink simulation softwares, build ride comfort weight coefficient optimization design Simulink emulation
Model, as shown in Figure 3；
2. ride comfort weight coefficient optimization design object function is set up
According to the ride comfort weight coefficient optimization design Simulink simulation models set up in 1. step, with ride comfort plus
Weight coefficient α1、α2、α3For design variable, using road roughness displacement as input stimulus, vehicle ride situation is imitated
Very, the vehicle body Vertical Acceleration root-mean-square value obtained by emulation is utilizedSet up ride comfort weight coefficient optimization design mesh
Scalar functions Jo(α1,α2,α3), i.e.,：
3. ride comfort weight coefficient Constrained Conditions in Optimal Design is set up
According to vehicle single-wheel unsprung mass m1=40kg, sprung mass m2=320kg, tire stiffness Kt=200000N/m,
Gravity acceleration g=9.8m/s2, and bump clearance of suspension [fd]=100mm, utilizes tire vertical deviation z1, vehicle body vertical deviation
z2, road roughness displacement q, and ride comfort weight coefficient α1、α2、α3, set up ride comfort weight coefficient optimization design constraint bar
Part, i.e.,
4. the optimization design of ride comfort weight coefficient
According to the ride comfort weight coefficient optimization design Simulink simulation models set up in 1. step, and 3. in step
The ride comfort weight coefficient Constrained Conditions in Optimal Design set up, with ride comfort weight coefficient α1、α2、α3For design variable, with road
Face unevenness displacement is asked using optimized algorithm and ride comfort weight coefficient optimization design mesh is set up in 2. step as input stimulus
Scalar functions Jo(α1,α2,α3) minimum value, try to achieve the optimum optimization design load of ride comfort weight coefficient, i.e. α1o=0.0108, α2o
=0.0506, α3o=0.9386；
(6) LQG Controller of Active Suspension Optimal Control Force UaoDesign：
I steps：According to the ride comfort weight coefficient α that 4. optimization order design is obtained in step (5)1o=0.0108, α2o=
0.0506、α3oThe weighting matrix expression formula Q (α determined in=0.9386, and step (3)1,α2,α3)、R(α1,α2,α3)、N(α1,
α2,α3), calculating obtains weighting matrices Q (0.0108,0.0506,0.9386), R (0.0108,0.0506,0.9386), N
(0.0108,0.0506,0.9386), i.e.,：
R (0.0108,0.0506,0.9386)=9.766 × 10-6,
Ii steps：According to the weighting square determined in the state matrix A and control matrix B, and i steps determined in step (2)
Battle array Q (0.0108,0.0506,0.9386), R (0.0108,0.0506,0.9386), N (0.0108,0.0506,0.9386), profit
The optimum control feedback gain matrix K for trying to achieve Active suspension LQG is calculated with the LQR functions in Matlabo, i.e.,：
Ko=[1247.4-270.4-17,563 18032.3 347.5]；
Iii steps：According to the Optimal Feedback gain matrix K determined in ii stepso, with unsteadiness of wheels speedAnd tire hangs down
To displacement z1, body vibrations speedAnd vehicle body vertical deviation z2With road roughness displacement q as state variable, it is determined that actively
The Optimal Control Force U of suspension LQG controllersao, i.e.,：
Under same vehicle structural parameters and driving cycle, wherein, vehicle traveling road conditions are B grades of road surfaces, vehicle traveling speed
V=72km/h is spent, Optimal Control Force is determined to Conventional wisdom method respectively and Optimal Control Force is determined using the Optimization Design Method
LQG control carry out model emulation, wherein, using Conventional wisdom method determine Optimal Control Force beThe vehicle body of two kinds of control methods obtained by emulation hangs down
To the correlation curve of vibration acceleration time-domain signal and vehicle body Vertical Acceleration power spectral density, respectively such as Fig. 4, Fig. 5 institute
Show, it is known that, the vertical vibration for significantly reducing vehicle body using the LQG Controller of Active Suspension designed by the Optimization Design Method accelerates
Degree, compared with Conventional wisdom design method, vehicle body Vertical Acceleration root-mean-square value reduces 53.0%, shows what is set up
The design method of vehicle active suspension LQG controller Optimal Control Forces is correct.
Claims (1)
1. the design method of vehicle active suspension LQG controller Optimal Control Forces, its specific design step is as follows：
(1) the 1/4 vehicle ride differential equation is set up：
According to vehicle single-wheel unsprung mass m1, sprung mass m2, suspension stiffness K2, tire stiffness Kt, Active suspension to be designed
Controling power Ua；With tire vertical deviation z1, vehicle body vertical deviation z2For coordinate；Using road roughness displacement q as input stimulus；Build
The vertical 1/4 vehicle ride differential equation, i.e.,：
(2) the state matrix A and control matrix B of LQG controls are determined：
According to vehicle single-wheel unsprung mass m1, sprung mass m2, suspension stiffness K2, tire stiffness Kt, Vehicle Speed v,
And filtering white noise road surface spatial-cut-off frequency n0c, the state matrix A and control matrix B of LQG controls are determined, is respectively：
(3) the weighting matrix expression formula of LQG controls is determined：
According to vehicle single-wheel unsprung mass m1, sprung mass m2, suspension stiffness K2, tire stiffness Kt, bump clearance of suspension
[fd], and gravity acceleration g, it is determined that on ride comfort weight coefficient α1、α2、α3State variable, control variable and state become
Amount and control variable cross-product term weighting matrix expression formula Q (α1,α2,α3)、R(α1,α2,α3)、N(α1,α2,α3), it is respectively：
Wherein,q3=1；α1It is the relative dynamic load weight coefficient of wheel, α2For suspension
With respect to dynamic deflection weight coefficient, α3For vehicle body vertical vibration relative acceleration weight coefficient；
(4) Active suspension LQG controling powers U is determinedaExpression formula：
I steps：Choose the initial value of ride comfort weight coefficient, i.e. α1=k1、α2=k2、α3=k3, wherein, k1, k2, k3Value
It is to be more than zero and the numerical value less than 1, and k1+k2+k3=1.0；
II steps：According to the ride comfort weighting coefficient initial values α chosen in I steps1=k1、α2=k2、α3=k3, and step (3)
The weighting matrix expression formula Q (α of middle determination1,α2,α3)、R(α1,α2,α3)、N(α1,α2,α3), calculating obtains weighting matrices Q (k1,
k2,k3)、R(k1,k2,k3)、N(k1,k2,k3)；
III steps：According to the weighting matrices Q determined in the state matrix A and control matrix B, and II steps determined in step (2)
(k1,k2,k3)、R(k1,k2,k3)、N(k1,k2,k3), the control for trying to achieve Active suspension LQG is calculated using the LQR functions in Matlab
Feedback gain matrix K processed；
IV steps：According to the feedback gain matrix K determined in III steps, with unsteadiness of wheels speedAnd tire vertical deviation z1、
Body vibrations speedAnd vehicle body vertical deviation z2With road roughness displacement q as state variable, determine that Active suspension LQG is controlled
Power U processedaExpression formula, i.e.,：
Wherein,For matrixTransposed matrix；
(5) optimization design of ride comfort weight coefficient：
1. ride comfort weight coefficient optimization design simulation model is built
According to the 1/4 vehicle ride differential equation set up in step (1), and the control that IV steps are tried to achieve in step (4)
Power U processeda, using Matlab/Simulink simulation softwares, build ride comfort weight coefficient optimization design Simulink emulation moulds
Type；
2. ride comfort weight coefficient optimization design object function is set up
According to the ride comfort weight coefficient optimization design Simulink simulation models set up in 1. step, system is weighted with ride comfort
Number α1、α2、α3For design variable, using road roughness displacement as input stimulus, vehicle ride situation is emulated,
Utilize the vehicle body Vertical Acceleration root-mean-square value obtained by emulationSet up ride comfort weight coefficient optimization design target
Function Jo(α1,α2,α3), i.e.,：
3. ride comfort weight coefficient Constrained Conditions in Optimal Design is set up
According to vehicle single-wheel unsprung mass m1, sprung mass m2, tire stiffness Kt, gravity acceleration g, and bump clearance of suspension
[fd], utilize tire vertical deviation z1, vehicle body vertical deviation z2, road roughness displacement q, and ride comfort weight coefficient α1、α2、
α3, ride comfort weight coefficient Constrained Conditions in Optimal Design is set up, i.e.,
4. the optimization design of ride comfort weight coefficient
Built according to the ride comfort weight coefficient optimization design Simulink simulation models set up in 1. step, and 3. in step
Vertical ride comfort weight coefficient Constrained Conditions in Optimal Design, with ride comfort weight coefficient α1、α2、α3For design variable, with road surface not
Pingdu displacement is asked using optimized algorithm and ride comfort weight coefficient optimization design target letter is set up in 2. step as input stimulus
Number Jo(α1,α2,α3) minimum value, corresponding design variable is the optimum optimization design load of ride comfort weight coefficient, i.e.,
α1o、α2o、α3o；
(6) LQG Controller of Active Suspension Optimal Control Force UaoDesign：
I steps：According to the ride comfort weight coefficient α that 4. optimization order design is obtained in step (5)1o、α2o、α3o, and step (3)
The weighting matrix expression formula Q (α of middle determination1,α2,α3)、R(α1,α2,α3)、N(α1,α2,α3), calculating obtains weighting matrices Q (α1o,
α2o,α3o)、R(α1o,α2o,α3o)、N(α1o,α2o,α3o)；
Ii steps：According to the weighting matrices Q determined in the state matrix A and control matrix B, and i steps determined in step (2)
(α1o,α2o,α3o)、R(α1o,α2o,α3o)、N(α1o,α2o,α3o), calculated using the LQR functions in Matlab and try to achieve Active suspension
LQG optimum control feedback gain matrix Ko；
Iii steps：According to the Optimal Feedback gain matrix K determined in ii stepso, with unsteadiness of wheels speedAnd the vertical position of tire
Move z1, body vibrations speedAnd vehicle body vertical deviation z2With road roughness displacement q as state variable, Active suspension is determined
The Optimal Control Force U of LQG controllersao, i.e.,：
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510645787.1A CN105159094B (en) | 2015-10-08 | 2015-10-08 | The design method of vehicle active suspension LQG controller Optimal Control Forces |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510645787.1A CN105159094B (en) | 2015-10-08 | 2015-10-08 | The design method of vehicle active suspension LQG controller Optimal Control Forces |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105159094A CN105159094A (en) | 2015-12-16 |
| CN105159094B true CN105159094B (en) | 2017-08-04 |
Family
ID=54799982
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510645787.1A Expired - Fee Related CN105159094B (en) | 2015-10-08 | 2015-10-08 | The design method of vehicle active suspension LQG controller Optimal Control Forces |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105159094B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10156197B1 (en) * | 2017-06-16 | 2018-12-18 | GM Global Technology Operations LLC | Model predictive control systems and methods for increasing computational efficiency |
| CN108345218A (en) * | 2018-02-27 | 2018-07-31 | 江苏大学 | Vehicle active suspension PID controller design method based on teaching optimization algorithm |
| CN109933886B (en) * | 2019-03-11 | 2022-07-12 | 桂林电子科技大学 | Method for optimizing suspension arrangement of cab of commercial vehicle |
| CN110329030B (en) * | 2019-05-10 | 2021-03-30 | 爱驰汽车有限公司 | Active suspension control method, system, device and storage medium |
| CN110568758B (en) * | 2019-09-12 | 2022-07-01 | 中汽研（天津）汽车工程研究院有限公司 | Parameter self-adaptive transverse motion LQR control method for automatically driving automobile |
| CN111487863B (en) * | 2020-04-14 | 2022-06-17 | 东南大学 | Active suspension reinforcement learning control method based on deep Q neural network |
| CN113352832A (en) * | 2021-07-06 | 2021-09-07 | 南昌大学 | Multi-target dynamic optimal active suspension control method based on pavement grade recognition |
| CN113887070A (en) * | 2021-10-21 | 2022-01-04 | 浙江吉利控股集团有限公司 | Vehicle ride comfort analysis method, device and equipment and storage medium |
| CN114282361A (en) * | 2021-12-17 | 2022-04-05 | 江苏大学 | Novel LQG control method |
| CN117521229B (en) * | 2023-12-27 | 2024-03-19 | 石家庄铁道大学 | Pavement displacement response detection method, system and storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4953089A (en) * | 1988-05-09 | 1990-08-28 | Lord Corporation | Hybrid analog digital control method and apparatus for estimation of absolute velocity in active suspension systems |
| ITSA20080021A1 (en) * | 2008-08-06 | 2010-02-06 | Gerardo Acocella | METHOD AND APPARATUS FOR CONTROL OF A SEMI-ACTIVE MOTORCYCLE SUSPENSION SYSTEM |
| CN103646280B (en) * | 2013-11-28 | 2016-03-02 | 江苏大学 | A kind of vehicle suspension system parameter optimization method based on particle cluster algorithm |
| CN104309437B (en) * | 2014-10-23 | 2017-05-31 | 山东理工大学 | The method for designing of vehicle air suspension non-linear rigidity real-time optimistic control |
-
2015
- 2015-10-08 CN CN201510645787.1A patent/CN105159094B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN105159094A (en) | 2015-12-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105159094B (en) | The design method of vehicle active suspension LQG controller Optimal Control Forces | |
| CN107791773B (en) | Whole vehicle active suspension system vibration control method based on specified performance function | |
| CN107092245B (en) | Hardware-in-loop simulation test bed for automobile dynamic chassis control system | |
| CN103942392B (en) | Automotive chassis technical parameter robust design method based on full life circle | |
| Swevers et al. | A model-free control structure for the on-line tuning of the semi-active suspension of a passenger car | |
| CN109334378B (en) | Vehicle ISD suspension active control method based on single neuron PID control | |
| CN108681257B (en) | Design method of controller of active anti-roll system | |
| CN109002599B (en) | Automobile ride comfort optimization analysis method based on Taguchi experiment | |
| CN110155103B (en) | High-speed train semi-active suspension control system and method based on LQG control | |
| CN103754081A (en) | Optimal fuzzy complex control method of vehicle nonlinear suspension system | |
| CN105676694A (en) | Intelligent sampling data output feedback control method for automobile suspension system | |
| CN105059078B (en) | Control method for automobile active suspension system with hysteresis actuator | |
| Corno et al. | Active control of aerodynamic surfaces for ride control in sport vehicles | |
| Corno et al. | Performance assessment of active aerodynamic surfaces for comfort and handling optimization in sport cars | |
| Cheng et al. | Active variable geometry suspension robust control for improved vehicle ride comfort and road holding | |
| CN105700350A (en) | Fuzzy sampling data control method for automobile suspension system | |
| CN110443003B (en) | Control and optimal design method of active stabilizer bar system | |
| CN105069260B (en) | High speed railway car two is the Optimization Design of vertical suspension Optimal damping ratio | |
| CN105117554B (en) | High speed railway car one is the design method of vertical suspension Optimal damping ratio | |
| CN105160103B (en) | The system of high speed railway car one and two be vertical suspension damping ratio cooperative optimization method | |
| CN105160180B (en) | High speed railway car two is the Analytic Calculation Method of vertical suspension Optimal damping ratio | |
| CN105069259B (en) | Low speed rail vehicle two is the Analytic Calculation Method of vertical suspension optimum damping ratio | |
| Li et al. | Study on PID control for semi-active air suspension for riding comfort | |
| Zhao et al. | Intelligent compound control of vehicle active suspension based on RBF neural network | |
| CN105224718B (en) | The system of high speed railway car two laterally suspends the Optimization Design of Optimal damping ratio |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170804 Termination date: 20201008 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |